Issues with and alternatives to Deep Learning approaches?
I have very little experience with ML/DL to call myself either practitioner, but here is my answer on the 1st question:

At its core DL solves well the task of classification. Not every practical problem can be rephrased in terms of classification. Classification domain needs to be known upfront. Although the classification can be applied to any type of data, it's necessary to train the NN with samples of the specific domain where it'll be applied. If the domain is switched at some point, while keeping the same model (NN structure), it'll have to be retrained with new samples. Furthermore, even the best classifiers have "gaps" - Adversarial Examples can be easily constructed from a training sample, such that changes are imperceptible to human, but are misclassified by the trained model.